Dataset for Solar-Powered Submersible Pump Systems

Wind direction measured by degrees from North 5. Humidity measured by percentage 6. Gust measured by (m/sec) 7.

Cloud cover measured by percentage. The energy generated by solar power is influenced by various environmental factors. For example, a higher UV index generally increases energy output since solar panels rely on sunlight, though prolonged exposure can degrade materials over time.

Air temperature, on the other hand, has a negative impact on the energy generated. However, while solar panels need sunlight, excessive heat reduces efficiency due to increased electrical resistance, therefore, wind speed can be beneficial in cooling the panels, helping to counteract temperature-related losses, but strong gusts may stress mounting structures. Wind direction indirectly affects performance by influencing cooling efficiency, depending on how air flows around the panels.

On the other hand, humidity reduces energy output because water vapor scatters sunlight, limiting the amount of radiation reaching the panels, and over time, excessive moisture can degrade panel components. Similarly, cloud cover reduces the effect of direct sunlight, leading to lower energy production. Overall, optimal conditions for solar energy include high UV levels, moderate temperatures, steady cooling winds, and minimal humidity or cloud cover.

The weather data were collected daily using the stormglass.io API given a certain position in terms of longitude and latitude. This data represents the parameters that affect the solar energy, hence the input power to the inverter motor. Data related to the inverter motor are: 1.

Frequency reference 2. Output frequency 3. Output current 4.

DC bus voltage 5. Output power 6. Output frequency fault 7.

Heatsink temperature 8. Proportional-Integral-Derivative (PID) controller output 9. PID input For more information about the dataset or the system please read and cite our publication: "AI-Driven Digital Twin for Solar-Powered Submersible Pump Systems: A Machine Learning Approach for Performance Optimization"